Concurrent Genetic Programming, Tartarus and Dancing Agents
Proceedings of the Second European Workshop on Genetic Programming
Genetic parallel programming: design and implementation
Evolutionary Computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Parallel evolution using multi-chromosome cartesian genetic programming
Genetic Programming and Evolvable Machines
Digital enzymes: agents of reaction inside robotic controllers for the foraging problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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The Digital Enzyme model of control is based on the bottom-up, reactive process of signal transduction found in cells. An earlier study applied a specific instance of the this model to the foraging problem. Here, we extend the system and use it to explore a fundamental question in both biology and evolutionary computation, namely, whether environmental complexity is a driving factor for an organism''s internal control structure. To address this question, we extended the original system to allow the open-ended evolution of the unique programs, instructions, and threads within each controller. With the extended model, we were able to evolve successful foraging strategies that nearly doubled the performance of strategies found in the earlier work. In response to increasing environmental complexity, we discovered a high degree of variation for the number of programs, threads, and instructions that produced successful strategies. These results imply that environmental complexity does not require evolutionary search methods to explore regions of the search space characterized by parallel and distributed control. However, strategies found within these regions were as successful as strategies governed by a single program and thread, highlighting the importance of evolutionary search techniques that enable the open-ended evolution of key internal control components.